Parallel Triangles Counting Using Pipelining

The generalized method to have a parallel solution to a computational problem, is to find a way to use Divide & Conquer paradigm in order to have processors acting on its own data and therefore all can be scheduled in parallel. MapReduce is an example of this approach: Input data is transformed by the mappers, in order to feed the reducers that can run in parallel. In general this schema gives efficient problem solutions, but it stops being true when the replication factor grows. We present another program schema that is useful for describing problem solutions, that can exploit dynamic pipeline parallelism without having to deal with replication factors. We present the schema in an example: counting triangles in graphs, in particular when the graph do not fit in memory. We describe the solution in NiMo, a graphical programming language that implements the implicitly functional parallel dataflow model of computation. The solution obtained using NiMo, is architecture agnostic and can be deployed in any parallel/distributed architecture adapting dynamically the processor usage to input characteristics.